Pub Date : 2024-01-05DOI: 10.1088/1361-6501/ad1ba4
Shengfan Chen, Xiaoxia Zheng
A rolling bearing fault diagnosis method based on improved symplectic geometry mode decomposition and feature selection was proposed to solve the problem of low fault identification due to the influence of noise on early bearing fault features. First, the symplectic geometry mode decomposition is improved to enhance its robustness in decomposing signals with noise, then the time domain, frequency domain, and time-frequency features of each symplectic geometric component are extracted as feature vectors. Second, a comprehensive feature selection strategy is proposed to select the optimal subset of features that are conducive to fault classification. Finally, considering the problem of low classification accuracy of a single machine learning model, the AdaBoost-WSO-SVM model is constructed for fault classification using the AdaBoost algorithm of integrated learning. Experimental decomposition of complex signals with noise indicates that the improved symplectic geometry mode decomposition is more effective compared to traditional symplectic geometry mode decomposition. Subsequently, multiple experiments were conducted using the bearing datasets from Case Western Reserve University (CWRU) and Jiangnan University (JNU). The experimental results reveal that, after comprehensive feature selection and ensemble learning pattern recognition experiments on the CWRU dataset, the average accuracy of fault diagnosis can reach 99.67%. On the JNU dataset, the proposed fault diagnosis method achieves an average accuracy of 95.03%. This suggests that, compared to other feature selection methods and classification models, the proposed approach in this paper exhibits higher accuracy and generalization capabilities.
{"title":"A bearing fault diagnosis method with improved symplectic geometry mode decomposition and feature selection","authors":"Shengfan Chen, Xiaoxia Zheng","doi":"10.1088/1361-6501/ad1ba4","DOIUrl":"https://doi.org/10.1088/1361-6501/ad1ba4","url":null,"abstract":"\u0000 A rolling bearing fault diagnosis method based on improved symplectic geometry mode decomposition and feature selection was proposed to solve the problem of low fault identification due to the influence of noise on early bearing fault features. First, the symplectic geometry mode decomposition is improved to enhance its robustness in decomposing signals with noise, then the time domain, frequency domain, and time-frequency features of each symplectic geometric component are extracted as feature vectors. Second, a comprehensive feature selection strategy is proposed to select the optimal subset of features that are conducive to fault classification. Finally, considering the problem of low classification accuracy of a single machine learning model, the AdaBoost-WSO-SVM model is constructed for fault classification using the AdaBoost algorithm of integrated learning. Experimental decomposition of complex signals with noise indicates that the improved symplectic geometry mode decomposition is more effective compared to traditional symplectic geometry mode decomposition. Subsequently, multiple experiments were conducted using the bearing datasets from Case Western Reserve University (CWRU) and Jiangnan University (JNU). The experimental results reveal that, after comprehensive feature selection and ensemble learning pattern recognition experiments on the CWRU dataset, the average accuracy of fault diagnosis can reach 99.67%. On the JNU dataset, the proposed fault diagnosis method achieves an average accuracy of 95.03%. This suggests that, compared to other feature selection methods and classification models, the proposed approach in this paper exhibits higher accuracy and generalization capabilities.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"106 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139383528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper proposes an improved method for model-based segmentation of curved and irregular mounded structures in 3D measurements. The proposed method divides the point cloud data into several levels according to the reasonable width calculated from the density of points, and then fits a curve model with 2D points to each level separately. The classification results of specific types are merged to obtain specific structural measurement data in 3D space. Experiments were conducted on the proposed method using the region growth algorithm (SRG) and the model-based segmentation method (MS) provided in the PCL library as the control group. The results show that the proposed method achieves higher accuracy with a mean intersection merge ratio (MloU) of more than 0.8238, which is at least 37.92% higher than SRG and MS. The proposed method is also faster with a time-consuming only 1/5 of SRG and 1/2 of MS. Therefore, the proposed method is an effective and efficient way to segment the measurement data of curved and irregular mounded structures in 3D measurements. The method proposed in this paper has also applied in the practical robotic grinding task, the root mean square error of the grinding amount is less than 2 mm, and good grinding results are achieved.grinding results are achieved.
{"title":"Enhanced Curve-Based Segmentation Method for Point Clouds of Curved and Irregular Structures","authors":"Limei Song, Zongyang Zhang, Chongdi Xu, Yangang Yang, Xinjun Zhu","doi":"10.1088/1361-6501/ad1ba1","DOIUrl":"https://doi.org/10.1088/1361-6501/ad1ba1","url":null,"abstract":"\u0000 This paper proposes an improved method for model-based segmentation of curved and irregular mounded structures in 3D measurements. The proposed method divides the point cloud data into several levels according to the reasonable width calculated from the density of points, and then fits a curve model with 2D points to each level separately. The classification results of specific types are merged to obtain specific structural measurement data in 3D space. Experiments were conducted on the proposed method using the region growth algorithm (SRG) and the model-based segmentation method (MS) provided in the PCL library as the control group. The results show that the proposed method achieves higher accuracy with a mean intersection merge ratio (MloU) of more than 0.8238, which is at least 37.92% higher than SRG and MS. The proposed method is also faster with a time-consuming only 1/5 of SRG and 1/2 of MS. Therefore, the proposed method is an effective and efficient way to segment the measurement data of curved and irregular mounded structures in 3D measurements. The method proposed in this paper has also applied in the practical robotic grinding task, the root mean square error of the grinding amount is less than 2 mm, and good grinding results are achieved.grinding results are achieved.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"89 7","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139381326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-05DOI: 10.1088/1361-6501/ad1ba5
Xinxin Li, Yiwen Bi, Weili Tang, H. Mao, Zhenfeng Huang
Nitriding treatment can improve the surface properties of workpieces, thus increasing the service life of the workpiece. The depth of nitriding layer is not only one of the important indexes for evaluating the nitriding effect, but also an important factor affecting the end-use performance of the workpiece. While the existing hardness and metallographic methods cannot meet the needs for non-destructive testing of nitriding layer depth in shaft parts. Therefore, a method using non-linear ultrasonic testing technology is proposed for non-destructive evaluation of nitriding layer depth. In this study, 1045 steel shaft specimens with different nitriding layer depths were prepared by a liquid salt bath nitriding method. The total depth of the nitriding layer was measured using a microhardness tester, and metallographic microscopy was applied to observe microstructure changes before and after nitriding treatment. With the proposed non-destructive method, the longitudinal critically refracted (LCR) wave mixing detection model was established and the ultrasonic nonlinear coefficients were used for characterizing the nitrided layer depths. Experimental results show that the LCR wave sum frequency (LCRWSF) detection model of ultrasonic nonlinear coefficient is better to characterize the nitriding layer depth of 1045 steel and have higher sensitivity. As a result, the LCRWSF model is more suitable to efficiently estimate the nitrided layer depth.
{"title":"Nitriding layer depth detection based on mixing frequency nonlinear ultrasonic parameters","authors":"Xinxin Li, Yiwen Bi, Weili Tang, H. Mao, Zhenfeng Huang","doi":"10.1088/1361-6501/ad1ba5","DOIUrl":"https://doi.org/10.1088/1361-6501/ad1ba5","url":null,"abstract":"\u0000 Nitriding treatment can improve the surface properties of workpieces, thus increasing the service life of the workpiece. The depth of nitriding layer is not only one of the important indexes for evaluating the nitriding effect, but also an important factor affecting the end-use performance of the workpiece. While the existing hardness and metallographic methods cannot meet the needs for non-destructive testing of nitriding layer depth in shaft parts. Therefore, a method using non-linear ultrasonic testing technology is proposed for non-destructive evaluation of nitriding layer depth. In this study, 1045 steel shaft specimens with different nitriding layer depths were prepared by a liquid salt bath nitriding method. The total depth of the nitriding layer was measured using a microhardness tester, and metallographic microscopy was applied to observe microstructure changes before and after nitriding treatment. With the proposed non-destructive method, the longitudinal critically refracted (LCR) wave mixing detection model was established and the ultrasonic nonlinear coefficients were used for characterizing the nitrided layer depths. Experimental results show that the LCR wave sum frequency (LCRWSF) detection model of ultrasonic nonlinear coefficient is better to characterize the nitriding layer depth of 1045 steel and have higher sensitivity. As a result, the LCRWSF model is more suitable to efficiently estimate the nitrided layer depth.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"103 11","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139383555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-05DOI: 10.1088/1361-6501/ad1ba6
Wei Hou, qichang zhang, Shu ying Hao, Kunpeng Zhang
Micro-electromechanical systems (MEMS) gyroscope has important applications in many fields such as aviation, spaceflight, weaponry and automatic driving. To improve the robustness and sensitivity, we design a novel dual-mass MEMS gyroscope based on the mode localization in this paper. The gyroscope structure consists of a pair of perturbation systems connected with weakly coupled resonator systems (WCRS). It has the advantage of eliminating the mode matching and achieving the mode localization effect. The dynamic behaviors of MEMS gyroscope are developed by the multi-scale method. The detection characteristics of amplitude ratio (AR) and amplitude difference (AD) are compared. Combining numerical simulation, we analyzed the influence of critical parameter. It is indicated that the sensitivity can reach up to 56199.78 ppm/°/s through AR output, which is two magnitudes higher than the traditional MEMS gyroscope. For the detection of micro-angular rate, the AD output has advantages in sensitivity, and AR output has a smaller nonlinearity error. In addition, structural parameters, especially the voltage of perturbation parallel plate, have a significant impact on system sensitivity. If the breakdown voltage meets condition, the sensitivity can be enhanced more than ten times by amplifying the voltage, which further broaden the application field of the MEMS gyroscope.
微机电系统（MEMS）陀螺仪在航空、航天、武器和自动驾驶等许多领域都有重要应用。为了提高陀螺仪的鲁棒性和灵敏度，我们在本文中设计了一种基于模式定位的新型双质量 MEMS 陀螺仪。该陀螺仪结构由一对与弱耦合谐振器系统（WCRS）相连的扰动系统组成。它具有消除模式匹配和实现模式定位效果的优点。采用多尺度方法研究了 MEMS 陀螺仪的动态行为。比较了振幅比（AR）和振幅差（AD）的检测特性。结合数值模拟，分析了关键参数的影响。结果表明，通过 AR 输出，灵敏度可达 56199.78 ppm/°/s，比传统的 MEMS 陀螺仪高出两个量级。对于微角速率的检测，AD 输出在灵敏度方面具有优势，而 AR 输出的非线性误差较小。此外，结构参数，尤其是扰动平行板的电压对系统灵敏度也有很大影响。如果击穿电压满足条件，通过放大电压可将灵敏度提高十倍以上，从而进一步拓宽 MEMS 陀螺仪的应用领域。
{"title":"Design and dynamic analysis of a highly sensitive MEMS gyroscope based on mode localization.","authors":"Wei Hou, qichang zhang, Shu ying Hao, Kunpeng Zhang","doi":"10.1088/1361-6501/ad1ba6","DOIUrl":"https://doi.org/10.1088/1361-6501/ad1ba6","url":null,"abstract":"\u0000 Micro-electromechanical systems (MEMS) gyroscope has important applications in many fields such as aviation, spaceflight, weaponry and automatic driving. To improve the robustness and sensitivity, we design a novel dual-mass MEMS gyroscope based on the mode localization in this paper. The gyroscope structure consists of a pair of perturbation systems connected with weakly coupled resonator systems (WCRS). It has the advantage of eliminating the mode matching and achieving the mode localization effect. The dynamic behaviors of MEMS gyroscope are developed by the multi-scale method. The detection characteristics of amplitude ratio (AR) and amplitude difference (AD) are compared. Combining numerical simulation, we analyzed the influence of critical parameter. It is indicated that the sensitivity can reach up to 56199.78 ppm/°/s through AR output, which is two magnitudes higher than the traditional MEMS gyroscope. For the detection of micro-angular rate, the AD output has advantages in sensitivity, and AR output has a smaller nonlinearity error. In addition, structural parameters, especially the voltage of perturbation parallel plate, have a significant impact on system sensitivity. If the breakdown voltage meets condition, the sensitivity can be enhanced more than ten times by amplifying the voltage, which further broaden the application field of the MEMS gyroscope.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"6 10","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139381107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-05DOI: 10.1088/1361-6501/ad19c0
Guo Yan, Yihuai Hu
Inter-turn short circuit (ITSC) and demagnetization of permanent magnet synchronous motors (PMSMs) can lead to serious ship accidents, timely and accurate fault diagnosis of these faults is very important. A multi-signal fusion fault diagnosis method (MD-CNN-BiLSTM) is proposed based on multi-scale residual dilated convolutional neural network (D-CNN) and bidirectional long and short-term memory (BiLSTM) for PMSM fault diagnosis. This method first takes three-phase current and vibration signals as input; uses a three-column parallel CNN structure with different scales to extract both global signal and local feature. A residual connection in the expanded CNN is then used to eliminate the problems of gradient disappearance or explosion; and finally, BiLSTM is used to further extract features and identify the fault. A 2.2 kW permanent magnet synchronous motor was used to build a fault simulation test rig. The motor stator was rewound to simulate the ITSC fault, and different sizes of permanent magnets were replaced to simulate demagnetization fault. ITSC, demagnetization and their coupled faults were simulated under 10 specific motor speeds and loads respectively. The test proved that the diagnostic accuracy of the proposed method was 4.2% higher than that of ordinary CNN and 29.06% higher than that of BiLSTM. It also had the best diagnostic effect under the noise interference of different intensities. It was verified that the proposed method has good noise interference and strong classification ability.
{"title":"Inter-turn short circuit and demagnetization fault diagnosis of ship PMSM based on multiscale residual dilated CNN and BiLSTM","authors":"Guo Yan, Yihuai Hu","doi":"10.1088/1361-6501/ad19c0","DOIUrl":"https://doi.org/10.1088/1361-6501/ad19c0","url":null,"abstract":"Inter-turn short circuit (ITSC) and demagnetization of permanent magnet synchronous motors (PMSMs) can lead to serious ship accidents, timely and accurate fault diagnosis of these faults is very important. A multi-signal fusion fault diagnosis method (MD-CNN-BiLSTM) is proposed based on multi-scale residual dilated convolutional neural network (D-CNN) and bidirectional long and short-term memory (BiLSTM) for PMSM fault diagnosis. This method first takes three-phase current and vibration signals as input; uses a three-column parallel CNN structure with different scales to extract both global signal and local feature. A residual connection in the expanded CNN is then used to eliminate the problems of gradient disappearance or explosion; and finally, BiLSTM is used to further extract features and identify the fault. A 2.2 kW permanent magnet synchronous motor was used to build a fault simulation test rig. The motor stator was rewound to simulate the ITSC fault, and different sizes of permanent magnets were replaced to simulate demagnetization fault. ITSC, demagnetization and their coupled faults were simulated under 10 specific motor speeds and loads respectively. The test proved that the diagnostic accuracy of the proposed method was 4.2% higher than that of ordinary CNN and 29.06% higher than that of BiLSTM. It also had the best diagnostic effect under the noise interference of different intensities. It was verified that the proposed method has good noise interference and strong classification ability.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"11 11","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139383099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-05DOI: 10.1088/1361-6501/ad1b9f
Zhenshi Sun, Zheng Guo
The automated analysis of optical fiber vibration sensing data has been highly demanded in engineering applications. Therefore, intrusion analysis, which aims at detecting, recognizing, and classifying intrusions, holds great importance for optical fiber vibration sensing. In this work, an intelligent intrusion detection scheme employing an improved high-efficiency feature extraction technique and utilizing a dual Mach-Zehnder interferometer (DMZI)-based optical fiber perimeter security system is proposed. So, the DMZI-based perimeter security system in practical settings can be successfully established. Specifically, time-frequency feature vectors with nine features are firstly constructed using a maximal overlap discrete wavelet transformation approach and a zero crossing rate method. Then, the feature vectors are classified into corresponding categories using a radial basis function neural network. The effectiveness of the proposed scheme has been validated using six types of human intrusions, such as knocking, climbing, waggling, cutting, crashing and kicking the fence. The results show that the given intrusions can be accurately and rapidly recognized by the proposed scheme. The average recognition rate of 95.0% is achieved, and the average processing time for each sample data is only 0.033 s, which is significantly lower than the sampling interval (0.3 s) in our experiment. It is believed that the proposed scheme holds promising potential in the field of optical fiber perimeter security systems.
{"title":"Intelligent intrusion detection for optical fiber perimeter security system based on an improved high efficiency feature extraction technique","authors":"Zhenshi Sun, Zheng Guo","doi":"10.1088/1361-6501/ad1b9f","DOIUrl":"https://doi.org/10.1088/1361-6501/ad1b9f","url":null,"abstract":"\u0000 The automated analysis of optical fiber vibration sensing data has been highly demanded in engineering applications. Therefore, intrusion analysis, which aims at detecting, recognizing, and classifying intrusions, holds great importance for optical fiber vibration sensing. In this work, an intelligent intrusion detection scheme employing an improved high-efficiency feature extraction technique and utilizing a dual Mach-Zehnder interferometer (DMZI)-based optical fiber perimeter security system is proposed. So, the DMZI-based perimeter security system in practical settings can be successfully established. Specifically, time-frequency feature vectors with nine features are firstly constructed using a maximal overlap discrete wavelet transformation approach and a zero crossing rate method. Then, the feature vectors are classified into corresponding categories using a radial basis function neural network. The effectiveness of the proposed scheme has been validated using six types of human intrusions, such as knocking, climbing, waggling, cutting, crashing and kicking the fence. The results show that the given intrusions can be accurately and rapidly recognized by the proposed scheme. The average recognition rate of 95.0% is achieved, and the average processing time for each sample data is only 0.033 s, which is significantly lower than the sampling interval (0.3 s) in our experiment. It is believed that the proposed scheme holds promising potential in the field of optical fiber perimeter security systems.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"25 6","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139383166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-05DOI: 10.1088/1361-6501/ad1ba0
Wenjun Zhou, Xiaoping Xiao, Zisheng Li, kai Zhang, Ruide He
Accurate tool wear monitoring is crucial for increasing tool life and machining productivity. Although many prediction models can achieve high prediction accuracy, there are problems such as poor stability in the face of different working conditions or tool signals. A tool wear prediction method based on improved deep extreme learning machines (DELM) was proposed as a solution to this issue; it uses the sparrow search algorithm (SSA) to upgrade the input weight of DELM to improve the model, and then extracts the time-domain, frequency-domain, and time-frequency domain characteristics from multi-sensor signals to construct and test the improved model SSA-DELM. The verification results show that the proposed model accurately reflects the tool wear. Meanwhile, the RMSE of the proposed model decreased by 53.39%, 19.95%, 43.86%, 23.80%, 24.80%, and 3.72%, respectively, and the MAE decreased by 67.81%, 17.87%, 32.70%, 29.90%, 30.30%, and 6.78%, respectively, compared to the with unimproved DELM, PSO-LSSVM, LSTM, SSAE, RNN, and DBO-DELM.
{"title":"Prediction tool wear using improved deep extreme learning machines based on the sparrow search algorithm","authors":"Wenjun Zhou, Xiaoping Xiao, Zisheng Li, kai Zhang, Ruide He","doi":"10.1088/1361-6501/ad1ba0","DOIUrl":"https://doi.org/10.1088/1361-6501/ad1ba0","url":null,"abstract":"\u0000 Accurate tool wear monitoring is crucial for increasing tool life and machining productivity. Although many prediction models can achieve high prediction accuracy, there are problems such as poor stability in the face of different working conditions or tool signals. A tool wear prediction method based on improved deep extreme learning machines (DELM) was proposed as a solution to this issue; it uses the sparrow search algorithm (SSA) to upgrade the input weight of DELM to improve the model, and then extracts the time-domain, frequency-domain, and time-frequency domain characteristics from multi-sensor signals to construct and test the improved model SSA-DELM. The verification results show that the proposed model accurately reflects the tool wear. Meanwhile, the RMSE of the proposed model decreased by 53.39%, 19.95%, 43.86%, 23.80%, 24.80%, and 3.72%, respectively, and the MAE decreased by 67.81%, 17.87%, 32.70%, 29.90%, 30.30%, and 6.78%, respectively, compared to the with unimproved DELM, PSO-LSSVM, LSTM, SSAE, RNN, and DBO-DELM.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"51 20","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139384155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-05DOI: 10.1088/1361-6501/ad1ba2
Meizhi Liu, Xiangyu Kong, Jiayu Luo, Lei Yang
Timely and accurate detection of incipient faults has attracted considerable attention and research interest in recent years, due to its potential for the prevention of serious safety incidents and for supporting preventive maintenance. However, most existing methods use single detection model, neglecting the coexistence of multiple features and the local data distribution information found in industrial scenes. To overcome this problem, an incipient fault detection method named multiple model ensemble and distribution dissimilarity analysis (MME-DISSIM) is proposed. First, various multivariate statistical analysis methods are employed as basic detectors to comprehensively capture the feature information hidden in the process data. Second, distribution dissimilarity analysis is performed to evaluate the dissimilarity between the current sliding window and each training subset. This evaluation allows for the calculation of weighting factors for each basic detector, which helps to preserve the local distribution information of the current sliding window. Third, ensemble learning is utilized to integrate the statistics from all basic detectors into two detection indices to determine the operation status of the system. In addition, two measurement metrics are defined to quantitatively analyze the fault level of incipient faults. Finally, several experiments on a numerical case, Tennessee Eastman process, and actual PROcess NeTwork Optimization are presented to verify the efficacy and superiority of the proposed method.
近年来，及时准确地检测萌芽故障引起了人们的广泛关注和研究兴趣，因为它具有防止严重安全事故和支持预防性维护的潜力。然而，现有方法大多采用单一检测模型，忽略了工业场景中多种特征并存的情况和局部数据分布信息。为了克服这一问题，本文提出了一种名为 "多模型集合和分布差异分析（MME-DISSIM）"的初期故障检测方法。首先，采用各种多元统计分析方法作为基本检测器，全面捕捉隐藏在过程数据中的特征信息。其次，进行分布不相似性分析，以评估当前滑动窗口与每个训练子集之间的不相似性。通过这种评估，可以计算出每个基本检测器的加权系数，这有助于保留当前滑动窗口的局部分布信息。第三，利用集合学习将所有基本检测器的统计数据整合为两个检测指数，以确定系统的运行状态。此外，还定义了两个测量指标，用于定量分析初发故障的故障级别。最后，介绍了在一个数值案例、田纳西州伊士曼过程和实际 PROcess NeTwork 优化中的几个实验，以验证所提方法的有效性和优越性。
{"title":"Incipient Fault Detection Based on Ensemble Learning and Distribution Dissimilarity Analysis in Multi-feature Processes","authors":"Meizhi Liu, Xiangyu Kong, Jiayu Luo, Lei Yang","doi":"10.1088/1361-6501/ad1ba2","DOIUrl":"https://doi.org/10.1088/1361-6501/ad1ba2","url":null,"abstract":"\u0000 Timely and accurate detection of incipient faults has attracted considerable attention and research interest in recent years, due to its potential for the prevention of serious safety incidents and for supporting preventive maintenance. However, most existing methods use single detection model, neglecting the coexistence of multiple features and the local data distribution information found in industrial scenes. To overcome this problem, an incipient fault detection method named multiple model ensemble and distribution dissimilarity analysis (MME-DISSIM) is proposed. First, various multivariate statistical analysis methods are employed as basic detectors to comprehensively capture the feature information hidden in the process data. Second, distribution dissimilarity analysis is performed to evaluate the dissimilarity between the current sliding window and each training subset. This evaluation allows for the calculation of weighting factors for each basic detector, which helps to preserve the local distribution information of the current sliding window. Third, ensemble learning is utilized to integrate the statistics from all basic detectors into two detection indices to determine the operation status of the system. In addition, two measurement metrics are defined to quantitatively analyze the fault level of incipient faults. Finally, several experiments on a numerical case, Tennessee Eastman process, and actual PROcess NeTwork Optimization are presented to verify the efficacy and superiority of the proposed method.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"38 9","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139381639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-05DOI: 10.1088/1361-6501/ad1ba3
Huoyao Xu, Xiangyu Peng, Junlang Wang, Jie Liu, Chaoming He
Domain adaptation (DA) is an effective solution for addressing the domain shift problem. However, existing DA techniques usually directly match the distributions of the data in the original feature space, where some of the features may be distorted by a large domain shift. Besides, geometric and clustering structures of the data, which play a significant role in revealing hidden failure patterns, are not considered in traditional DA methods. To tackle the above issues, a new joint soft clustering and distribution alignment with graph embedding (JSCDA-GE) method is proposed. Specifically, weighted subspace alignment (WSA) is proposed to align bases of source and target subspaces by combining instance reweighting and subspace alignment strategies. Then, JSCDA-GE formulates an objective function by incorporating dynamic distribution alignment (DDA), soft large margin clustering (SLMC), and graph embedding (GE) in a unified structural risk minimization (SRM) framework. Ultimately, JSCDA-GE aims to learn a generalization classifier for fault diagnosis. Its effectiveness and superiority have been confirmed through thirty-six tasks on two bearing databases.
域自适应（DA）是解决域偏移问题的有效方案。然而，现有的 DA 技术通常直接匹配原始特征空间中的数据分布，其中一些特征可能会因较大的域偏移而失真。此外，数据的几何结构和聚类结构对揭示隐藏的故障模式起着重要作用，但传统的数据分析方法并没有考虑到这一点。针对上述问题，我们提出了一种新的图嵌入联合软聚类和分布对齐（JSCDA-GE）方法。具体来说，该方法提出了加权子空间配准（WSA），通过结合实例重权和子空间配准策略来配准源子空间和目标子空间的基数。然后，JSCDA-GE 通过将动态分布对齐（DDA）、软大余量聚类（SLMC）和图嵌入（GE）纳入一个统一的结构风险最小化（SRM）框架来制定目标函数。最终，JSCDA-GE 的目标是学习用于故障诊断的泛化分类器。它的有效性和优越性已在两个轴承数据库的三十六个任务中得到证实。
{"title":"Adaptive graph-guided joint soft clustering and distribution alignment for cross-load and cross-device rotating machinery fault transfer diagnosis","authors":"Huoyao Xu, Xiangyu Peng, Junlang Wang, Jie Liu, Chaoming He","doi":"10.1088/1361-6501/ad1ba3","DOIUrl":"https://doi.org/10.1088/1361-6501/ad1ba3","url":null,"abstract":"\u0000 Domain adaptation (DA) is an effective solution for addressing the domain shift problem. However, existing DA techniques usually directly match the distributions of the data in the original feature space, where some of the features may be distorted by a large domain shift. Besides, geometric and clustering structures of the data, which play a significant role in revealing hidden failure patterns, are not considered in traditional DA methods. To tackle the above issues, a new joint soft clustering and distribution alignment with graph embedding (JSCDA-GE) method is proposed. Specifically, weighted subspace alignment (WSA) is proposed to align bases of source and target subspaces by combining instance reweighting and subspace alignment strategies. Then, JSCDA-GE formulates an objective function by incorporating dynamic distribution alignment (DDA), soft large margin clustering (SLMC), and graph embedding (GE) in a unified structural risk minimization (SRM) framework. Ultimately, JSCDA-GE aims to learn a generalization classifier for fault diagnosis. Its effectiveness and superiority have been confirmed through thirty-six tasks on two bearing databases.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"30 9","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139383036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-04DOI: 10.1088/1361-6501/ad1b34
Lina Huang, Dengfeng Wang, Xiaolin Cao, Xiaopeng Zhang, Bingtong Huang, Yang He, Gottfried Grabner
Analyzing and mitigating wind noise in automobiles under high-speed conditions is a significant issue within the realm of Noise, Vibration, and Harshness (NVH). Due to the intricate nature of aeroacoustics generation mechanisms, current conventional methods for wind noise prediction have limitations. Hence, deep learning methods are introduced to investigate wind noise in the side window area of an automotive clay model.During aeroacoustic wind tunnel experiments, side window vibration data and noise data from the driver were collected under vehicle speed conditions of 100 km/h, 120 km/h, and 140 km/h, respectively. These data samples were obtained to be used for training and validation of the wind noise model. Convolutional Neural Networks (CNN) and Long Short-Term Memory Neural Network (LSTM) algorithms were separately employed to reveal the complex nonlinear relationship between wind noise and its influencing factors, leading to the establishment of a wind noise prediction model.Simultaneously, these two deep learning methods were compared with Backpropagation Neural Networks (BPNN), Extreme Learning Machines (ELM), and Support Vector Regression (SVR) methods. Our findings revealed that the LSTM wind noise prediction model not only exhibits higher accuracy but also demonstrates superior generalization capabilities, thereby substantiating the superiority of this method.
{"title":"Deep Learning-Based Wind noise Prediction Study for Automotive Clay Model","authors":"Lina Huang, Dengfeng Wang, Xiaolin Cao, Xiaopeng Zhang, Bingtong Huang, Yang He, Gottfried Grabner","doi":"10.1088/1361-6501/ad1b34","DOIUrl":"https://doi.org/10.1088/1361-6501/ad1b34","url":null,"abstract":"\u0000 Analyzing and mitigating wind noise in automobiles under high-speed conditions is a significant issue within the realm of Noise, Vibration, and Harshness (NVH). Due to the intricate nature of aeroacoustics generation mechanisms, current conventional methods for wind noise prediction have limitations. Hence, deep learning methods are introduced to investigate wind noise in the side window area of an automotive clay model.During aeroacoustic wind tunnel experiments, side window vibration data and noise data from the driver were collected under vehicle speed conditions of 100 km/h, 120 km/h, and 140 km/h, respectively. These data samples were obtained to be used for training and validation of the wind noise model. Convolutional Neural Networks (CNN) and Long Short-Term Memory Neural Network (LSTM) algorithms were separately employed to reveal the complex nonlinear relationship between wind noise and its influencing factors, leading to the establishment of a wind noise prediction model.Simultaneously, these two deep learning methods were compared with Backpropagation Neural Networks (BPNN), Extreme Learning Machines (ELM), and Support Vector Regression (SVR) methods. Our findings revealed that the LSTM wind noise prediction model not only exhibits higher accuracy but also demonstrates superior generalization capabilities, thereby substantiating the superiority of this method.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"44 4","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139385897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：